Development of a novel competitive ELISA to investigate exposure of animals to SARS-CoV-2
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the COVID19-causing virus, is a zoonotic pathogen. There is concern that it may spillover into wildlife species which may then serve as reservoirs for future infection of humans, domestic animals, or other wildlife species. Furthermore, impacts of the virus on potentially susceptible wildlife species are currently unknown. There is, therefore, an urgent need to develop a single test that could be used for the serosurveillance of multiple wildlife species for exposure to SARS-CoV-2. While serological tests to detect antibodies in SARS-CoV-2 infected and/or exposed human patients have been developed, few assays have the capacity to detect antibodies in a wide variety of species. Here, we describe the development of a competitive enzyme-linked immunosorbent assay (cELISA) to detect SARS-CoV-2 antibodies in animals for which species-specific reagents are not available. This cELISA was developed to detect SARS-CoV-2 spike 1 (S1), spike 2 (S2) and nucleocapsid (N) specific antibodies and was validated using sera from experimentally infected hamsters. We further validated our cELISA by comparing it with results obtained from the surrogate virus neutralization test (cPASS, GenScript) and indirect ELISA using anti-hamster horse radish peroxidase (HRP) conjugated reagents. This cELISA will have broad applications in screening potential animal reservoirs for SARS-CoV-2, and uses multiple targets, including more conserved structural proteins which are subjected to less selective immunological pressure. These would allow detection of exposure to variants missed by conventional assays that target antibodies against the viral receptor binding domain. This assay will be a valuable tool which can be implemented in surveillance programs investigating evidence of exposure to SARS-CoV-2 in multiple domestic, captive, or wild animal species, and in studies investigating impacts of SARS-CoV-2 on wildlife populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".